LOCAL PECULIARITY ORIENTED DATA MINING AND ITS APPLICATION IN OUTLIER DETECTION

Jian Yang (), Ning Zhong (), Yiyu Yao () and Jue Wang ()
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Jian Yang: International WIC Institute, Beijing University of Technology, Beijing, China
Ning Zhong: International WIC Institute, Beijing University of Technology, Beijing, China;
Yiyu Yao: International WIC Institute, Beijing University of Technology, Beijing, China;
Jue Wang: State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

International Journal of Information Technology & Decision Making (IJITDM), 2012, vol. 11, issue 06, 1155-1181

Abstract: Peculiarity oriented mining (POM), aimed at discovering peculiarity rules hidden in a dataset, is a data mining method. Peculiarity factor (PF) is one of the most important concepts in POM. In this paper, it is proved that PF can accurately characterize the peculiarity of data sampled from a normal distribution. However, for a general one-dimensional distribution, it does not have the property. A local version of PF, called LPF, is proposed to solve the difficulty. LPF can effectively describe the peculiarity of data sampled from a continuous one-dimensional distribution. Based on LPF, a framework of local peculiarity oriented mining is presented, which consists of two steps, namely, peculiar data identification and peculiar data analysis. Two algorithms for peculiar data identification and a case study of peculiar data analysis are given to make the framework practical. Experiments on several benchmark datasets show their good performance.

Keywords: Data mining; peculiarity factor; local peculiarity factor; local peculiarity oriented mining; outlier detection (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622012500319

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